Deep learning for automated contouring of neurovascular structures on magnetic resonance imaging for prostate cancer patients.
Phys Imaging Radiat Oncol
; 26: 100453, 2023 Apr.
Article
em En
| MEDLINE
| ID: mdl-37312973
ABSTRACT
Background and purpose:
Manual contouring of neurovascular structures on prostate magnetic resonance imaging (MRI) is labor-intensive and prone to considerable interrater disagreement. Our aim is to contour neurovascular structures automatically on prostate MRI by deep learning (DL) to improve workflow and interrater agreement. Materials andmethods:
Segmentation of neurovascular structures was performed on pre-treatment 3.0 T MRI data of 131 prostate cancer patients (training [n = 105] and testing [n = 26]). The neurovascular structures include the penile bulb (PB), corpora cavernosa (CCs), internal pudendal arteries (IPAs), and neurovascular bundles (NVBs). Two DL networks, nnU-Net and DeepMedic, were trained for auto-contouring on prostate MRI and evaluated using volumetric Dice similarity coefficient (DSC), mean surface distances (MSD), Hausdorff distances, and surface DSC. Three radiation oncologists evaluated the DL-generated contours and performed corrections when necessary. Interrater agreement was assessed and the time required for manual correction was recorded.Results:
nnU-Net achieved a median DSC of 0.92 (IQR 0.90-0.93) for the PB, 0.90 (IQR 0.86-0.92) for the CCs, 0.79 (IQR 0.77-0.83) for the IPAs, and 0.77 (IQR 0.72-0.81) for the NVBs, which outperformed DeepMedic for each structure (p < 0.03). nnU-Net showed a median MSD of 0.24 mm for the IPAs and 0.71 mm for the NVBs. The median interrater DSC ranged from 0.93 to 1.00, with the majority of cases (68.9%) requiring manual correction times under two minutes.Conclusions:
DL enables reliable auto-contouring of neurovascular structures on pre-treatment MRI data, easing the clinical workflow in neurovascular-sparing MR-guided radiotherapy.
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Base de dados:
MEDLINE
Tipo de estudo:
Guideline
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article